{"id":"W3214476327","doi":"10.3390/sym13112166","title":"Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy","year":2021,"lang":"en","type":"article","venue":"Symmetry","topic":"Software Engineering Research","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Yayasan UTP; Universiti Teknologi Petronas","keywords":"Computer science; Feature selection; Overfitting; Data mining; Maxima and minima; Curse of dimensionality; Machine learning; Ranking (information retrieval); Artificial intelligence; Software; Classifier (UML); Feature (linguistics); Process (computing); Pattern recognition (psychology); Artificial neural network; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000725688,0.001249973,0.001633263,0.002874661,0.0003995455,0.0007399709,0.001053895,0.0006075311,0.001026103],"category_scores_gemma":[0.002349978,0.0003223932,0.001176328,0.001320585,0.0002670361,0.0008019334,0.0005102896,0.0004309342,0.0004701895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000424327,"about_ca_system_score_gemma":0.000903585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007324806,"about_ca_topic_score_gemma":0.005101036,"domain_scores_codex":[0.9992182,0.0000880726,0.00006288997,0.0001919984,0.0003249639,0.0001138357],"domain_scores_gemma":[0.9987174,0.0003681181,0.0001358876,0.0001124725,0.000600391,0.00006572724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003628037,0.0004077756,0.02081077,0.0001367032,0.0002197094,0.0006224047,0.0001382829,0.235527,0.0211187,0.001096394,0.005670976,0.7138885],"study_design_scores_gemma":[0.000017647,0.0001180279,0.003835875,0.000008630969,0.00004767436,0.0001220912,0.00002489464,0.9913731,0.003297609,0.0005453355,0.0005905748,0.0000187152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2102952,0.0007840623,0.7831616,0.0002078789,0.00009636881,0.0002012734,0.0004055201,0.003242155,0.001606007],"genre_scores_gemma":[0.8988628,0.0002450816,0.09746793,0.00008863486,0.00005581191,0.0002137118,0.0009992551,0.00008579519,0.001980809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007324806,"threshold_uncertainty_score":0.01456434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617267138903952,"score_gpt":0.27165605243976,"score_spread":0.2554833810507205,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}